{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "##解决图例中文乱码，设置参数\n",
    "matplotlib.rcParams['font.family']=['sans-serif']\n",
    "matplotlib.rcParams['font.sans-serif']=['Songti SC']#显示中文\n",
    "matplotlib.rcParams['font.serif']=['Songti SC']\n",
    "matplotlib.rcParams['axes.unicode_minus']=False #正常显示符号\n",
    "from math import pi\n",
    "\n",
    "#屏蔽警告信息\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "##图标来源 https://www.data-to-viz.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#创建数据\n",
    "names='groupA', 'groupB', 'groupC', 'groupD',\n",
    "values=[12,11,3,30]\n",
    "#设置颜色\n",
    "colors = ['#4F6272', '#B7C3F3', '#DD7596', '#8EB897']\n",
    "\n",
    "#wedgeprops:设置边界线颜色及其他参数\n",
    "plt.pie(values, labels=names, labeldistance=1.15,colors=colors,wedgeprops = { 'linewidth' : 1, 'edgecolor' : 'white' },)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 甜甜圈图形（改在饼图，挖空中间部分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#创建数据\n",
    "names = ['groupA', 'groupB', 'groupC', 'groupD']\n",
    "size = [12,11,3,30]\n",
    " \n",
    "# 创建实体白圈图\n",
    "my_circle = plt.Circle( (0,0), 0.7, color='white')\n",
    "\n",
    "# 创建饼图\n",
    "plt.pie(size, labels=names, colors=['red','green','blue','skyblue'],wedgeprops = { 'linewidth' : 7, 'edgecolor' : 'white' })\n",
    "p = plt.gcf()\n",
    "#增加my_circle 白圈实体图\n",
    "p.gca().add_artist(my_circle)\n",
    "\n",
    "# Show the graph\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
